Snowflake COF-C02 Practice Test - Questions Answers, Page 39
List of questions
Related questions
Question 381

How should clustering be used to optimize the performance of queries that run on a very large table?
Explanation:
For optimizing the performance of queries that run on a very large table, it is recommended to choose one high cardinality column as the clustering key.This helps to co-locate similar rows in the same micro-partitions, improving scan efficiency in queries by skipping data that does not match filtering predicates4.
References:[COF-C02] SnowPro Core Certification Exam Study Guide
Question 382

What step can reduce data spilling in Snowflake?
Explanation:
To reduce data spilling in Snowflake, using a larger virtual warehouse is effective because it provides more memory and local disk space, which can accommodate larger data operations and minimize the need to spill data to disk or remote storage1. References: [COF-C02] SnowPro Core Certification Exam Study Guide
Question 383

Which VALIDATION_MODE value will return the errors across the files specified in a COPY command, including files that were partially loaded during an earlier load?
Explanation:
TheRETURN_ERRORSvalue in theVALIDATION_MODEoption of the COPY command instructs Snowflake to validate the data files and return errors encountered across all specified files, including those that were partially loaded during an earlier load2. References: [COF-C02] SnowPro Core Certification Exam Study Guide
Question 384

Which solution improves the performance of point lookup queries that return a small number of rows from large tables using highly selective filters?
Explanation:
The search optimization service improves the performance of point lookup queries on large tables by using selective filters to quickly return a small number of rows.It creates an optimized data structure that helps in pruning the micro-partitions that do not contain the queried values3. References: [COF-C02] SnowPro Core Certification Exam Study Guide
Question 385

Which Snowflake object does not consume any storage costs?
Explanation:
Temporary tables do not consume any storage costs in Snowflake because they only exist for the duration of the session that created them and are automatically dropped when the session ends, thus incurring no long-term storage charges4. References: [COF-C02] SnowPro Core Certification Exam Study Guide
Question 386

Which function unloads data from a relational table to JSON?
Explanation:
TheTO_JSONfunction is used to convert a VARIANT value into a string containing the JSON representation of the value. This function is suitable for unloading data from a relational table to JSON format. References: [COF-C02] SnowPro Core Certification Exam Study Guide
Question 387

Which commands can only be executed using SnowSQL? (Select TWO).
Explanation:
TheLISTandPUTcommands are specific to SnowSQL and cannot be executed in the web interface or other SQL clients.LISTis used to display the contents of a stage, andPUTis used to upload files to a stage. References: [COF-C02] SnowPro Core Certification Exam Study Guide
Question 388

What is the relationship between a Query Profile and a virtual warehouse?
Explanation:
A Query Profile provides detailed execution information for a query, which can be used to analyze the performance and behavior of queries. This information can help users optimize and right-size their virtual warehouses for better efficiency. References: [COF-C02] SnowPro Core Certification Exam Study Guide
Question 389

What is the primary purpose of a directory table in Snowflake?
Explanation:
A directory table in Snowflake is used to store file-level metadata about the data files in a stage. It is conceptually similar to an external table and provides information such as file size, last modified timestamp, and file URL. References: [COF-C02] SnowPro Core Certification Exam Study Guide
Question 390

Which statements describe benefits of Snowflake's separation of compute and storage? (Select TWO).
Explanation:
Snowflake's architecture allows for the independent scaling of compute resources, meaning you can increase or decrease the computational power as needed without affecting storage. This separation also means that storage can grow independently of compute usage, allowing for more flexible and cost-effective data management.
Question